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We present progress in utilizing a machine learning (ML) assisted optimization framework to study the trends in a parameter space defined by spectrally shaped, high-intensity, petawatt-class (8 J, 45 fs) laser pulses interacting with solid targets and give the first simulation-based overview of predicted trends. A neural network (NN) incorporating uncertainty quantification is trained to predict the number of hot electrons generated by the laser–target interaction as a function of pulse shaping parameters. The predictions of this NN serve as the basis function for a Bayesian optimization framework to navigate this space. For post-experimental evaluation, we compare two separate neural network (NN) models. One is based solely on data from experiments, and the other is trained only on ensemble particle-in-cell simulations. Reviewing the predicted and observed trends across the experiment-capable laser parameter search space, we find that both ML models predict a maximal increase in hot electron generation at a level of approximately 12%–18%; however, no statistically significant enhancement was observed in experiments. On direct comparison of the NN models, the average discrepancy is 8.5%, with a maximum of 30%. Since shot-to-shot fluctuations in experiments affect the observations, we evaluate the behavior of our optimization framework by performing virtual experiments that vary the number of repeated observations and the noise levels. Here, we discuss the implications of such a framework for future autonomous exploration platforms in high-repetition-rate experiments. [ABSTRACT FROM AUTHOR]

Background Hybrid theatres facilitate higher quality angiography and simultaneous employment of open and endovascular techniques. We compared treatment approaches and radiation exposure following hybrid theatre introduction in January 2016. Methods 6-year retrospective review of all aortic interventions (January 2015 - March 2021) at a single tertiary vascular unit, from a prospective operative database. 'Pre-hybrid' cases in traditional theatres were compared to those in the 'post-hybrid' period (hybrid and traditional theatres). Results From 3885 vascular procedures, there were 184 aortic cases in the pre-hybrid period and 886 post-hybrid (hybrid theatre n=539, traditional theatres n=369). Proportionally, endovascular procedures increased from 61% (pre-hybrid) to 97% (new hybrid theatre) (p < 0.05). Of the cases in traditional theatres, post hybrid introduction; 94% (347) open and 6% (22) endovascular or combined. 212 (40%) of all endovascular cases in the hybrid setting were complex (TEVAR, FEVAR, BEVAR & ChEVAR/ChEVAS). The average FEVAR DAP vastly reduced following hybrid introduction (403 vs 233 (Gy cm²) (p<0.005)). Conclusion The ratio of interventions clearly define a drive towards aortic endovascular approaches. Hybrid theatres facilitate a wider range of techniques for aortic procedures and significantly reduces radiation dosing in complex FEVAR. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to evaluate the management practices and outcomes in children with sinogenic intracranial suppuration. Method: This was a retrospective cohort study in a single paediatric tertiary unit that included patients younger than 18 years with radiologically confirmed intracranial abscess, including subdural empyema and epidural or intraparenchymal abscess secondary to sinusitis. Main outcomes studied were rate of return to the operating theatre, length of hospital stay, death in less than 90 days and neurological disability at 6 months. Results: A cohort of 39 consecutive patients presenting between 2000 and 2020 were eligible for inclusion. Subdural empyema was the most common intracranial complication followed by extradural abscess and intraparenchymal abscess. Mean length of hospital stay was 42 days. Sixteen patients were managed with combined ENT and neurosurgical interventions, 15 patients underwent ENT procedures alone and 4 patients had only neurosurgical drainage. Four patients initially underwent non-operative management. The rates of return to the operating theatre, neurological deficits and 90-day mortality were 19, 9 and 3, respectively, and were comparable across the 4 treatment arms. In the univariate logistic regression, only the size of an intracranial abscess was found be associated with an increased likelihood of return to the operating theatre, whereas combined ENT and neurosurgical intervention did not result in improved outcomes. Conclusion: Sinogenic intracranial abscesses are associated with significant morbidity and mortality. The size of an intracranial abscess has a strong association with a need for a revision surgery. [ABSTRACT FROM AUTHOR]

Abstract Background Asthma is a common chronic respiratory inflammatory disease that adversely affects patients’ quality of life (QoL) and overall well-being. When asthma is not adequately controlled, there is a higher risk of exacerbations and hospitalizations, thereby increasing the direct and indirect costs associated with the treatment and productivity loss. Overreliance on SABA and underutilization of ICS in the management of asthma can result in suboptimal treatment and poor asthma control. Patients who visit the emergency department are more likely to have poorly controlled asthma. Ensuring that these patients are provided with an evidence-based treatment plan during discharge can help reduce the risk of future exacerbations and consequently reduce the burden on the UAE healthcare system. Methods A joint task force comprising experts from the Emirates Society of Emergency Medicine (ESEM) and Emirates Thoracic Society (ETS) reviewed published evidence and updated guidelines in asthma management to optimize the post-discharge recommendations. Results The ESEM-ETS experts’ joint task force has developed a step-by-step plan for emergency department/hospital discharge, which is based on the GINA 2023 guideline recommendations and the medications available in the UAE. By adhering to this structured plan, emergency department physicians can play a crucial role in improving asthma care, long-term patient outcomes, and the utilization of healthcare resources. Conclusions Prioritizing patient education and ensuring patients are equipped with the best-suited asthma treatment plans prior to discharge can help ED physicians improve patient outcomes and reduce healthcare resource utilization in UAE hospitals.

Computer models of intense, laser-driven ion acceleration require expensive particle-in-cell simulations that may struggle to capture all the multi-scale, multi-dimensional physics involved at reasonable costs. Explored is an approach to ameliorate this deficiency using a multi-fidelity framework that can incorporate physical trends and phenomena at different levels. As the basis for this study, an ensemble of approximately 8000 1D simulations was generated to buttress separate ensembles of hundreds of higher fidelity 1D and 2D simulations. Using transfer learning with deep neural networks, one can reproduce the results of more complex physics at a much lower cost. The networks trained in this fashion can, in turn, act as surrogate models for the simulations themselves, allowing for quick and efficient exploration of the parameter space of interest. Standard figures-of-merit were used as benchmarks such as the hot electron temperature, peak ion energy, conversion efficiency, and so on. We can rapidly identify and explore under what conditions differing fidelities become an important effect and search for outliers in feature space. [ABSTRACT FROM AUTHOR]

Oral cavity structures receive abundant perfusion from the branches of the external carotid artery. Although hemorrhage is a common complication of oral cancer, bleeding from a cancerous mouth can be a devastating event in the emergency department. Lingual artery embolization is a minimally invasive interventional radiology procedure that has rarely been reported in the literature. This case involves a patient with tongue cancer who experienced massive oral bleeding leading to cardiac arrest. Fortunately, the patient was successfully resuscitated, and spontaneous circulation was restored. Diagnostic angiography confirmed the presence of pseudoaneurysm in the lingual artery. Ultimately, bleeding was definitively controlled through bilateral lingual artery embolization. This case underscores the critical importance of a multidisciplinary approach involving emergency physicians, otolaryngologists, and interventional radiologists in managing complex vascular lesions in the head and neck. Furthermore, it contributes to the growing body of evidence supporting the use of embolization techniques as a valuable tool in the management of vascular lesions in the head and neck region.

Background: The non-vitamin K antagonist oral anticoagulants (NOACs) have become the mainstay anticoagulation therapy for patients requiring oral anticoagulants (OACs) in the Gulf Council Cooperation (GCC) countries. The frequency of NOAC-associated major bleeding is expected to increase in the Emergency Department (ED). Nonetheless, we still lack local guidelines and recommendations for bleeding management in the region. The present Delphi-based consensus aims to establish a standardized and evidence-based clinical care pathway for managing NOAC-associated major bleeding in the Kingdom of Saudi Arabia (KSA) and the United Arab Emirates (UAE). Methods: We adopted a three-step modified Delphi method to develop evidence-based recommendations through two voting rounds and an advisory meeting between the two rounds. A panel of 11 experts from the KSA and UAE participated in the consensus development. Results: Twenty-eight statements reached the consensus level. These statements addressed key aspects of managing major bleeding events associated with NOACs, including the increased use of NOAC in clinical practice, clinical care pathways, and treatment options. Conclusion: The present Delphi consensus provides evidence-based recommendations and protocols for the management of NOAC-associated bleeding in the region. Patients with major NOAC-induced bleeding should be referred to a well-equipped ED with standardized management protocols. A multidisciplinary approach is recommended for establishing the association between NOAC use and major bleeding. Treating physicians should have prompt access to specific reversal agents to optimize patient outcomes. Real-world evidence and national guidelines are needed to aid all stakeholders involved in NOAC-induced bleeding management.

Abstract: Two‐dimensional embeddings remain the dominant approach to visualize high dimensional data. The choice of embeddings ranges from highly non‐linear ones, which can capture complex relationships but are difficult to interpret quantitatively, to axis‐aligned projections, which are easy to interpret but are limited to bivariate relationships. Linear project can be considered as a compromise between complexity and interpretability, as they allow explicit axes labels, yet provide significantly more degrees of freedom compared to axis‐aligned projections. Nevertheless, interpreting the axes directions, which are often linear combinations of many non‐trivial components, remains difficult. To address this problem we introduce a structure aware decomposition of (multiple) linear projections into sparse sets of axis‐aligned projections, which jointly capture all information of the original linear ones. In particular, we use tools from Dempster‐Shafer theory to formally define how relevant a given axis‐aligned project is to explain the neighborhood relations displayed in some linear projection. Furthermore, we introduce a new approach to discover a diverse set of high quality linear projections and show that in practice the information of k linear projections is often jointly encoded in ∼ k axis‐aligned plots. We have integrated these ideas into an interactive visualization system that allows users to jointly browse both linear projections and their axis‐aligned representatives. Using a number of case studies we show how the resulting plots lead to more intuitive visualizations and new insights. [ABSTRACT FROM AUTHOR]

This paper describes experiments in a subscale axial turbine stage equipped with an axially overlapping radial-clearance seal at the disk cavity rim and a labyrinth seal radially inboard which divides the disk cavity into a rim cavity and an inner cavity. An orifice model of the rim seat is presented; values of ingestion and egress discharge coefficients based on the model and experimental data are reported for a range of cavity purge flow rate. In the experiments, time-averaged pressure distribution was measured in the main gas annulus and in the disk cavity; also measured was the time-averaged ingestion into the cavity. The pressure and ingestion data were combined to obtain the discharge coefficients. Locations on the vane platform 1 mm upstream of its lip over two vane pitches circumferentially defined the main gas annulus pressure; in the rim cavity, locations at the stator sutface in the radially inner part of the "seal region" over one vane pitch defined the cavity pressure. For the sealing effectiveness, two locations in the rim cavity at the stator sutface, one in the "mixing region" and the other radially further inward at the beginning of the stator boundary layer were considered. Two corresponding sets of ingestion and egress discharge coefficients are reported. The ingestion discharge coefficient was found to decrease in magnitude as the purge flow rate increased; the egress discharge coefficient increased with purge flow rate. The discharge coefficients embody fluid-mechanical effects in the ingestion and egress flows. Additionally, the minimum purge flow rate required to prevent ingestion was estimated for each experiment set and is reported. It is suggested that the experiments were in the combined ingestion (Cl) region with externally induced (El) ingestion being the dominant contributor. [ABSTRACT FROM AUTHOR]

We introduce a novel interactive framework for visualizing and exploring high-dimensional datasets based on subspace analysis and dynamic projections. We assume the high-dimensional dataset can be represented by a mixture of low-dimensional linear subspaces with mixed dimensions, and provide a method to reliably estimate the intrinsic dimension and linear basis of each subspace extracted from the subspace clustering. Subsequently, we use these bases to define unique 2D linear projections as viewpoints from which to visualize the data. To understand the relationships among the different projections and to discover hidden patterns, we connect these projections through dynamic projections that create smooth animated transitions between pairs of projections. We introduce the view transition graph, which provides flexible navigation among these projections to facilitate an intuitive exploration. Finally, we provide detailed comparisons with related systems, and use real-world examples to demonstrate the novelty and usability of our proposed framework. [ABSTRACT FROM AUTHOR]

The PROBIES diagnostic is a new, highly flexible, imaging and energy spectrometer designed for laser-accelerated protons. The diagnostic can detect low-mode spatial variations in the proton beam profile while resolving multiple energies on a single detector or more. When a radiochromic film stack is employed for "single-shot mode," the energy resolution of the stack can be greatly increased while reducing the need for large numbers of films; for example, a recently deployed version allowed for 180 unique energy measurements spanning ∼3 to 75 MeV with <0.4 MeV resolution using just 20 films vs 180 for a comparable traditional film and filter stack. When utilized with a scintillator, the diagnostic can be run in high-rep-rate (>Hz rate) mode to recover nine proton energy bins. We also demonstrate a deep learning-based method to analyze data from synthetic PROBIES images with greater than 95% accuracy on sub-millisecond timescales and retrained with experimental data to analyze real-world images on sub-millisecond time-scales with comparable accuracy.

Objective: Emergency abdominal surgery has poorer outcomes and higher mortality rates, compared with elective surgery. Serious morbidity or mortality occurs in up to 40% of patients. No information is available with regard to the outcome of patients undergoing emergency abdominal surgery in rural Australia., Methods: Patients undergoing emergency abdominal surgery in a 110-bed rural surgical centre in South Australia over a 5 year period (January 2010-December 2014) were included in the study. Patient data were retrieved using the hospital database and review of patient records., Results: A total of 4396 general surgical emergency admissions was recorded. Emergency admissions without intervention, endoscopic intervention only, appendectomy, cholecystectomy or urological or gynaecological diagnoses were excluded from mortality analysis. The remaining 237 patients underwent major abdominal emergency surgery for bowel obstruction (benign and malignant: n = 143, 60%), injury/inflammation/perforation/peritonitis (n = 85, 36%) or haemorrhage/ischaemia (n = 9, 3.8%). Thirty- (n = 9) and 90- (n = 12) day mortality rates were 3.8% and 5.1%, respectively., Conclusion: Emergency abdominal surgery can be safely provided in non-metropolitan Australian centres, with a low 30-day mortality rate of 3.8% and a 90-day mortality rate of 5.1%. This compares well with results published by other national and international investigators., (© 2018 National Rural Health Alliance Ltd.)

Building highly nonlinear and nonparametric models is central to several state-of-the-art machine learning systems. Kernel methods form an important class of techniques that induce a reproducing kernel Hilbert space (RKHS) for inferring non-linear models through the construction of similarity functions from data. These methods are particularly preferred in cases where the training data sizes are limited and when prior knowledge of the data similarities is available. Despite their usefulness, they are limited by the computational complexity and their inability to support end-to-end learning with a task-specific objective. On the other hand, deep neural networks have become the de facto solution for end-to-end inference in several learning paradigms. In this paper, we explore the idea of using deep architectures to perform kernel machine optimization, for both computational efficiency and end-to-end inferencing. To this end, we develop the deep kernel machine optimization framework, that creates an ensemble of dense embeddings using Nyström kernel approximations and utilizes deep learning to generate task-specific representations through the fusion of the embeddings. Intuitively, the filters of the network are trained to fuse information from an ensemble of linear subspaces in the RKHS. Furthermore, we introduce the kernel dropout regularization to enable improved training convergence. Finally, we extend this framework to the multiple kernel case, by coupling a global fusion layer with pretrained deep kernel machines for each of the constituent kernels. Using case studies with limited training data, and lack of explicit feature sources, we demonstrate the effectiveness of our framework over conventional model inferencing techniques.

Objective: To explore the risk factors for ward and paediatric assessment unit (PAU) admissions from the emergency department (ED)., Design: Prospective observational study., Setting and Patients: Febrile children attending a large tertiary care ED during the winter of 2014-2015., Main Outcome Measures: Ward and PAU admissions, National Institute for Health and Care Excellence (NICE) guidelines classification, reattendance to the ED within 28 days and antibiotic use., Results: A total of 1097 children attending the children's ED with fever were analysed. Risk factors for PAU admission were tachycardia (RR=1.1, 95% CI (1 to 1.1)), ill-appearance (RR=2.2, 95% CI (1.2 to 4.2)), abnormal chest findings (RR=2.1, 95% CI (1.2 to 4.3)), categorised as NICE amber (RR 1.7 95% CI (1.2 to 2.5)). There was a 30% discordance between NICE categorisation at triage and statistical internal validation. Predictors of ward admission were a systemic (RR=6.9, 95% CI (2.4 to 19.8)) or gastrointestinal illness (RR=3.8, 95% (1.4 to 10.4)) and categorised as NICE Red (RR=5.9, 95% CI (2.2 to 15.3)). Only 51 children had probable bacterial pneumonia (4.6%), 52 children had a proven urinary tract infection (4.2%), with just 2 (0.2%) positive blood cultures out of 485 (44%) children who received an antibiotic. 15% of all children reattended by 28 days and were more likely to have been categorised as Amber and had investigations on initial visit., Conclusions: Risk factors for PAU and ward admissions are different in this setting with high reattendance rates and very low proportion of confirmed/probable serious bacterial infections. Future studies need to focus on reducing avoidable admissions and antibiotic treatment., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.)